A Neurocognitive Framework of Reality Construction: Active Inference, Predictive Processing, and Computational Consciousness (Part 1)
Zenodo (CERN European Organization for Nuclear Research) May 24, 2026 DOI: 10.5281/zenodo.20367152 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Neurocognitive Computational model Consciousness Bridging networking Cognition Construct python library Blueprint Cognitive architecture Cognitive science Artificial intelligence Cognitive systems Cognitive neuroscience Cognitive model Artificial neural network Bayesian network Computational neuroscience Pipeline software Human–computer interaction Bayesian probability |
| Key points | Proposes that biological neural networks construct subjective reality through active inference, predictive processing, and hierarchical Bayesian inference by integrating interoceptive and exteroceptive signals to minimize free energy. |
Abstract
This manuscript introduces the foundational architecture of Neurocognitive Reality Construction Theory, a comprehensive multi-part framework modeling how physical cognitive systems actively construct subjective reality. Grounded in active inference, predictive processing, and hierarchical Bayesian inference, Volume I establishes the core mechanisms through which biological neural networks integrate interoceptive and exteroceptive signals to maintain structural integrity and minimize free energy. By bridging computational neuroscience with the operational demands of fear-response modeling and environmental mapping, this work provides a scalable, mechanism-oriented blueprint for understanding human conscious experience. This text represents Part 1 of a multi-volume theoretical series, laying the structural and mathematical groundwork for subsequent engineering applications in autonomous computational cognitive architectures.